Parameter recommendation method of clothes processing equipment, electronic equipment and clothes processing equipment

Abstract

The invention relates to the field of clothes processing, in particular to a parameter recommendation method of clothes processing equipment, electronic equipment and the clothes processing equipment, and the method comprises the steps that in response to parameter setting operation for the clothes processing equipment, at least one setting parameter is obtained, and each setting parameter corresponds to a selected parameter type; arranging the corresponding selected parameter types according to a setting sequence of the setting parameters to obtain a first setting sequence; screening out a recommendation parameter group from historical use data of clothes processing equipment in a preset region according to the first set sequence; and recommending to-be-selected parameter types different from the selected parameter types in the recommendation parameter group and recommendation parameters corresponding to the to-be-selected parameter types. The purpose of identifying the user identity by using the first set sequence is achieved, and the parameter recommendation accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of clothing processing, and in particular, to a parameter recommendation method for clothing processing equipment, an electronic device, and a clothing processing equipment. Background Art

[0002] When using a clothes processing device, users need to select operating parameters according to their own needs, such as operating mode, operating time, operating speed, etc. When there are many operating parameters to be selected, it undoubtedly increases the burden on users and easily reduces the user experience of the clothes processing device.

[0003] In order to solve the problem of heavy burden on users when selecting operating parameters, current clothing processing equipment will automatically recommend some operating parameters to users to reduce the user's selection work and thus reduce the user's burden.

[0004] However, the current operating parameter recommendation method recommends operating parameters to users based on the number of times the operating parameters are used, that is, the operating parameters that are used the most are recommended to users. However, users in different geographical locations often need to choose different operating parameters due to the influence of the geographical environment. For example, for users in the south, the temperature is higher, so they often choose a shorter dehydration time. For users in the north, the winter is longer, so they often choose a longer dehydration time and increase the drying process. Based on this, the recommendation accuracy of the current operating parameter recommendation method is low. Summary of the invention

[0005] Embodiments of the present application provide a parameter recommendation method for a clothing processing device, an electronic device, and a clothing processing device, so as to at least solve the technical problem of low accuracy of parameter recommendation.

[0006] According to a first aspect of an embodiment of the present application, a parameter recommendation method for a laundry processing device is provided, the method comprising: Acquiring a set parameter and a target region where the clothes treating device is located, wherein the set parameter includes a parameter having a value set based on a setting operation for a value of the parameter of the clothes treating device; Obtaining a target setting sequence according to the setting sequence of the set parameters; According to the target setting sequence and the target region, filtering out a recommended parameter group from historical usage data of the corresponding region, wherein the historical usage data includes usage data of the laundry processing equipment in the corresponding region; A parameter in the recommended parameter group that is different from the set parameter is recommended as a recommended parameter, and a value corresponding to the recommended parameter is recommended as a recommended value.

[0007] With this embodiment, since the recommended parameter group is screened from the historical usage data of the corresponding area according to the target area, the use location of the recommended parameter group and the current use location of the clothing processing device belong to the same area or a similar area, which helps to improve the compatibility of the recommended parameter group with the user's geographical location, thereby improving the accuracy of the recommendation. Furthermore, since the target setting sequence can reflect the user's personal habits and personal needs to a certain extent, the recommended parameter group screened according to the target setting sequence is easy to meet the user's personal habits and personal needs, which improves the compatibility of the recommended parameter group with the user, thereby further improving the accuracy of the recommendation.

[0008] In combination with the first aspect, in an optional implementation of the embodiment of the present application, the historical usage data includes a plurality of parameter groups, the parameter groups including historical setting parameters having a historical setting sequence and historical values ​​corresponding to each of the historical setting parameters; The step of selecting a recommended parameter group from historical usage data of a corresponding region according to the target setting sequence and the target region includes: using the historical usage data of the clothes treating equipment in the target area as target historical usage data; Determining whether the historical setting sequence of each of the parameter groups in the target historical usage data includes the target setting sequence; If the historical setting sequence includes the target setting sequence, the corresponding parameter group is used as the recommended parameter group.

[0009] With this implementation, each parameter group includes a historical setting sequence. The target historical usage data is first determined according to the target area, and then the target setting sequence is searched in the historical setting sequence corresponding to the target historical usage data to obtain a recommended parameter group. This is convenient and quick, and helps to ensure the accuracy of the recommended parameter group according to the target area.

[0010] In combination with the first aspect, in an optional implementation of the embodiment of the present application, the set parameter, the value corresponding to the set parameter, the historical setting parameter and the historical value are represented by characters, the target setting sequence is formed by arranging the characters corresponding to the set parameter to form a first character string, and the historical setting sequence is formed by arranging the characters corresponding to the historical setting parameter to form a second character string; The determining whether the historical setting sequence of each parameter group in the target historical usage data includes the target setting sequence includes: It is determined whether the second character string corresponding to each of the parameter groups in the target historical usage data includes the first character string.

[0011] With this implementation, the set parameters, values, historical set parameters and historical values ​​are all represented by characters. On the one hand, characters take up less space, which helps to reduce memory usage. On the other hand, the process of determining whether the target device set sequence is included in the historical set sequence is changed to determining whether the second string contains the first string. This makes comparison between strings more convenient and helps to improve efficiency.

[0012] In combination with the first aspect, in an optional implementation manner of the embodiment of the present application, the determining whether the second character string corresponding to each of the parameter groups in the target historical usage data contains the first character string includes: Searching for the first character of the first string in the second string, if the first character of the first string exists in the second string, numbering the first characters in the order in which they appear in the second string to obtain n positions of the first characters, where n is a positive integer; Compare the characters after the first character in the second character string one by one to see if they are the same as the corresponding characters in the first character string; If the characters after the first character in the second character string are all the same as the corresponding characters in the first character string, it is determined that the second character string contains the first character string; If any character after the first position of the first character in the second character string is different from the corresponding character in the first character string, then compare the characters after the second position of the first character to see if they are the same as the corresponding characters in the first character string one by one, until the characters after the last position of the first character are compared with the corresponding characters in the first character string; If any character after the last first character in the second character string is different from the corresponding character in the first character string, it is determined that the second character string does not contain the first character string.

[0013] By adopting this implementation method, the position where the first character appears in the second character string is first determined, and then a search is performed according to the position, which is beneficial to reducing the number of searches and improving search efficiency, thereby helping to improve parameter recommendation efficiency.

[0014] In combination with the first aspect, in an optional implementation manner of the embodiment of the present application, the determining whether the second character string corresponding to each of the parameter groups in the target historical usage data contains the first character string includes: The first character string is compared with the second character string corresponding to each of the parameter groups in the target historical usage data using a preset character string matching algorithm to determine whether the second character string contains the first character string.

[0015] By adopting this implementation method, the string matching algorithm can reduce the number of searches, which is beneficial to reducing the number of searches and improving search efficiency, thereby helping to improve parameter recommendation efficiency.

[0016] In combination with the first aspect, in an optional implementation manner of the embodiment of the present application, the method further includes: Whenever the position of the nth first character is changed to the position of the next first character, the number of changes is accumulated to obtain the number of changes corresponding to each second character string; The corresponding recommended parameter groups are arranged according to the number of changes, so as to recommend the recommended parameters and the recommended values ​​according to the arrangement order of the recommended parameter groups.

[0017] With this implementation, the fewer the number of changes, the higher the similarity of the corresponding recommended parameter groups, which is conducive to arranging the recommended parameters and recommended values ​​of the recommended parameter group with the highest similarity in the front, thereby improving the user experience.

[0018] In combination with the first aspect, in an optional implementation manner of the embodiment of the present application, the taking a parameter in the recommended parameter group that is different from the set parameter as a recommended parameter and recommending a value corresponding to the recommended parameter as a recommended value include: The recommendation is performed after converting the recommendation parameter and the recommendation value from the corresponding characters into corresponding identifiers according to a preset character replacement table, wherein the identifier includes at least one of text and numbers, and wherein the type of text is determined according to the target area.

[0019] By adopting this implementation method, after the characters are replaced with the words used in the target area, it is easy for users to understand and helps to improve the user experience.

[0020] In combination with the first aspect, in an optional implementation manner of the embodiment of the present application, the method further includes: determining a standby operation mode of the laundry treatment device according to the set parameters and the target area; Determining the parameters to be set according to the waiting operation mode; Comparing the parameters to be set with the set parameters to obtain the parameters to be recommended; If the parameters to be recommended include the parameters to be recommended that are multi-character, then performing the step of filtering out a recommended parameter group from historical usage data of a corresponding region according to the target setting sequence and the target region; If the parameters to be recommended are all single-character parameters to be recommended, the parameter group including the mode to be operated is determined from the historical usage data corresponding to the target area, and the parameter group with the most usage times is recommended.

[0021] With this implementation, for a single character, recommendations are made directly based on the number of times it is used, which helps to improve recommendation efficiency.

[0022] In combination with the first aspect, in an optional implementation manner of the embodiment of the present application, the method further includes: If the laundry processing device is in a parameter waiting state, determining whether the setting operation is performed; If the setting operation is not performed, the parameter group that is used most frequently in the historical usage data is recommended according to the target area.

[0023] With this implementation, in the case where no setting operation is performed, the parameter group that is used most frequently in the historical usage data is directly recommended to the user. For users who always use the same set of parameters, this is beneficial to improving the accuracy and efficiency of parameter recommendations.

[0024] In combination with the first aspect, in an optional implementation of the embodiment of the present application, before filtering out the recommended parameter group from the historical usage data of the corresponding region according to the target setting order and the target region, the method further includes: Acquire at least one identification parameter corresponding to the set parameter, including a key pressing area, a key pressing strength, a time interval between two adjacent keys being triggered, and a total setting duration of the set parameter; determining a user identity based on the identification parameters and the target setting sequence; Different parameter recommendation strategies are used to recommend parameters according to the historical usage of the clothing processing device, the current day usage of the clothing processing device and the identity of the user.

[0025] This implementation method uses identification parameters and target setting sequence to determine the user identity, which helps to improve the accuracy of the user identity. After determining the user identity, different parameter recommendation strategies are used to recommend parameters based on historical usage, current usage, and user identity, so that more factors are considered in the parameter recommendation process, which is conducive to improving the accuracy of parameter recommendation.

[0026] In combination with the first aspect, in an optional implementation of the embodiment of the present application, the parameter recommendation is performed using different parameter recommendation strategies according to the historical usage of the clothing processing device, the current day usage of the clothing processing device and the user identity, including: If the historical usage indicates that the laundry treatment device is used once within a preset time period, the parameter recommendation strategy adopted is: executing the method of filtering out a recommended parameter group from the historical usage data of the corresponding region according to the target setting sequence and the target region; And / or, if the historical usage indicates that the clothing treatment device is used multiple times within a preset time period, and the usage on the day and the user identity indicate that the same user uses the clothing treatment device multiple times, the parameter recommendation strategy adopted is: recommending a parameter group corresponding to the user identity and the current time from the historical usage data of the clothing treatment device as a recommended parameter group; And / or, if the historical usage indicates that the clothing processing device is used multiple times within a preset time period, and the usage on the day and the user identity indicate that multiple users use the clothing processing device multiple times, the parameter recommendation strategy adopted is: based on the target setting order and at least one of the user's age group, gender and clothing type, a recommended parameter group is filtered out from the historical usage data of the clothing processing device for recommendation.

[0027] By adopting this implementation method, three parameter recommendation strategies can be set, so that during the parameter recommendation process, three different situations can be distinguished according to historical usage, number of times used on the day and user identity, so that the corresponding parameter recommendation strategy can be adopted for each situation, thereby improving the accuracy and flexibility of parameter recommendation.

[0028] In combination with the first aspect, in an optional implementation manner of the embodiment of the present application, the method further includes: Determine whether the percentage of single usage of the laundry processing device in all the time periods in the historical usage exceeds a preset percentage threshold; If yes, determining that the historical usage indicates that the laundry treatment device is used once within a preset time period; Otherwise, it is determined that the historical usage indicates that the laundry treatment device has been used multiple times within a preset time period.

[0029] By adopting this implementation method, a percentage calculation method is used to determine whether the number of times the clothing processing device is used as represented by the historical usage is a single time or multiple times. The calculation is simple and helps to reduce the occupation of computing resources.

[0030] According to a second aspect of an embodiment of the present application, an electronic device is provided, the electronic device comprising a memory and a processor; The memory is used to store computer programs; The processor is used to execute the computer program to implement the steps of the method described above.

[0031] According to a third aspect of an embodiment of the present application, there is provided a clothing processing device, comprising the electronic device described above or using the method described above to perform parameter recommendations.

[0032] In combination with the third aspect, in an optional implementation of an embodiment of the present application, the clothing processing device includes at least one of a washing machine, a dryer, a washer-dryer and a clothing care machine.

[0033] The technical effects obtained by the above-mentioned second to third aspects are similar to the technical effects obtained by the corresponding technical means in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a flow chart of a parameter recommendation method for a clothing processing device provided in an embodiment of the present application; Figure 2 is another flow chart of a parameter recommendation method for a clothing processing device provided in an embodiment of the present application; Figure 3 is a character replacement table in a parameter recommendation method for a clothing processing device provided in an embodiment of the present application; Figure 4 is an application flow chart of a parameter recommendation method for a clothing processing device provided in an embodiment of the present application; Figure 5 It is a schematic diagram of parameter arrangement in a washing machine provided in an embodiment of the present application.

[0035] Figure 6 It is a schematic diagram of the KMP algorithm matching process provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0037] It should be understood that the "plurality" mentioned in this article refers to two or more. In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, the words "first", "second" and so on are used to distinguish between the same items or similar items with basically the same functions and effects. Those skilled in the art can understand that the words "first", "second" and so on do not limit the quantity and execution order, and the words "first", "second" and so on do not limit certain different In addition, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, product, or apparatus.

[0038] Parameters: refers to features that need to be set before or when the clothes processing device is running. For example, the operation mode, operation time, temperature, number of times, reservation time, speed, etc. can all be used as parameters. After certain parameters are set with corresponding values, derived parameters will appear. For example, when the value of the operation mode is the washing mode, parameters such as washing time and washing temperature will also appear. The above features can all be regarded as parameters. In other words, there are multiple parameters in this embodiment, and as long as they are items in the clothes processing device that can be set with corresponding values, they can all be regarded as parameters.

[0039] Value: Corresponding to the parameter, different parameters have different values. For example, for the parameter of operation mode, its value is usually expressed in Chinese characters, such as washing mode, dehydration mode, drying mode, etc. For the parameter of operation time, its value is usually expressed in time, such as 20min, 50min, etc. For the parameter of temperature, its value is usually expressed in numbers, such as 40, 50, etc. The default unit is degrees Celsius. In other words, when the clothing processing device is running, various functions or various operating conditions are based on the value of the corresponding parameter. When the user sets the clothing processing device, the value of each parameter is entered.

[0040] When using a clothes processing device, users need to select operating parameters according to their own needs, such as operating mode, operating time, operating speed, etc. When there are many operating parameters to be selected, it undoubtedly increases the burden on users and easily reduces the user experience of the clothes processing device.

[0041] In order to solve the problem of heavy burden on users when selecting operating parameters, current clothing processing equipment will automatically recommend some operating parameters to users to reduce the user's selection work and thus reduce the user's burden.

[0042] However, the current operating parameter recommendation method recommends operating parameters to users based on the number of times the operating parameters are used, that is, the operating parameters that are used the most are recommended to users. However, users in different geographical locations often need to choose different operating parameters due to the influence of the geographical environment. For example, for users in the south, the temperature is higher, so they often choose a shorter dehydration time. For users in the north, the winter is longer, so they often choose a longer dehydration time and increase the drying process. Based on this, the recommendation accuracy of the current operating parameter recommendation method is low.

[0043] Based on this, the embodiment of the present application provides a parameter recommendation method for a clothes processing device, referring to Figure 1 The flowchart of the parameter recommendation method for a clothing processing device shown is a schematic diagram, and the method includes the following processing procedures.

[0044] S100: Acquire set parameters and a target area where the laundry processing device is located.

[0045] The set parameters include parameters whose values ​​are set based on a setting operation of a value of the parameter of the laundry treating apparatus.

[0046] A setting operation refers to an operation related to the setting of a parameter value. The setting operation may be triggered by a user, for example, the user sets the value of one or several parameters by pressing a button on the clothing processing device. This setting process is the setting operation. The setting operation may also be triggered by a controller, for example, after the user starts the clothing processing device and sets the value of one or several parameters on a mobile phone, the mobile phone transmits the value set by the user to the clothing processing device. When the clothing processing device receives the data transmitted by the mobile phone, it is deemed that a setting operation has occurred.

[0047] Based on the setting operation, it is possible to determine which parameters have been set with values, so that the parameters with set values ​​are regarded as set parameters, and accordingly, the parameters without set values ​​can be understood as unset parameters. It should be noted that different operating modes of the clothing processing equipment require different parameters to be set, and the number of parameters to be set is also different. If the set parameters include the operating mode parameter, it is possible to determine which parameters are not set based on the value corresponding to the operating mode, so as to obtain the unset parameters. In other embodiments, all parameters of the clothing processing equipment except the set parameters can also be determined as unset parameters.

[0048] The target area can be preset or acquired in real time. For example, after the installation of the clothing processing equipment is completed, the location of the clothing processing equipment is recorded to obtain the target area. When the target area needs to be obtained, it can be directly searched in the storage space according to the unique number or unique identifier of the clothing processing equipment. For another example, the clothing processing equipment is associated with a positioning instrument, such as GPS or Beidou. The positioning instrument can be installed in the space where the clothing processing equipment is located, such as the user's home, or it can be installed on the clothing processing equipment, so that when the target area needs to be obtained, the location information uploaded by the positioning instrument can be received. Among them, the target area can be expressed by longitude and latitude, or by the name of the area, and this embodiment does not specifically limit this.

[0049] S102: Obtain a target setting sequence according to a setting sequence of set parameters.

[0050] Regarding the target setting sequence, it should be noted that when the user sets the clothing processing device, he does not directly set the parameters, but sets the values. However, each set value corresponds to a parameter. Therefore, by monitoring the setting sequence of the values, the setting sequence of the set parameters can be obtained, and thus the target setting sequence can be obtained. For ease of understanding, the target setting sequence is, for example: operation mode, number of times, temperature. In other words, the user first sets the value of the operation mode, and then sets the number of times and temperature values ​​in turn.

[0051] S104: According to the target setting sequence and the target region, a recommended parameter group is screened out from the historical usage data of the corresponding region.

[0052] The historical usage data includes usage data of clothing treatment equipment in a corresponding area.

[0053] It should be noted that the historical usage data refers to the relevant data of the previous operation of the clothing treatment device. The historical usage data includes setting data of parameters and their values. Therefore, the setting data including the target setting sequence can be found in the historical usage data through the target setting sequence, so as to obtain the recommended parameter group.

[0054] That is to say, each time the laundry treatment device is operated, it will record the parameters set by the user, the values ​​corresponding to each parameter, and the order in which the parameters are set. These data are stored in the form of parameter groups and become historical usage data. When the setting operation is responded to and the target setting order is obtained, the relevant or similar parameter groups can be found in the historical usage data according to the target setting order, and these parameter groups can be used as recommended parameter groups.

[0055] The historical usage data of clothing treatment equipment in different regions are stored in different storage spaces, or are distinguished by different identifiers, so that after the target region is determined, the historical usage data of the corresponding region can be found according to the target region. It should be noted that the corresponding region can be the same region as the target region, or a region close to the target region, such as a region 100 kilometers or farther or closer to the target region, or a region with the same climate as the target region, such as both of them have a marine climate.

[0056] S106: Recommend parameters in the recommended parameter group that are different from the set parameters as recommended parameters and values ​​corresponding to the recommended parameters as recommended values.

[0057] That is, the recommended parameter group includes multiple parameters, each of which has a corresponding value. First, the parameters that are the same as the set parameters are screened out, and the parameters that are different from the set parameters in the recommended parameter group are obtained. The different parameters are used as recommended parameters, and then the values ​​corresponding to the different parameters are used as recommended values ​​and recommended to the user.

[0058] With this embodiment, since the recommended parameter group is screened from the historical usage data of the corresponding area according to the target area, the use location of the recommended parameter group and the current use location of the clothing processing device belong to the same area or a similar area, which helps to improve the compatibility of the recommended parameter group with the user's geographical location, thereby improving the accuracy of the recommendation. Furthermore, since the target setting sequence can reflect the user's personal habits and personal needs to a certain extent, the recommended parameter group screened according to the target setting sequence is easy to meet the user's personal habits and personal needs, which improves the compatibility of the recommended parameter group with the user, thereby further improving the accuracy of the recommendation.

[0059] In a possible embodiment of the present application, the historical usage data includes a plurality of parameter groups, and the parameter groups include historical setting parameters having a historical setting sequence and historical values ​​corresponding to each historical setting parameter.

[0060] Based on the target setting sequence and target region, the recommended parameter groups are filtered from the historical usage data of the corresponding region, including: using historical usage data of clothes processing equipment in a target area as target historical usage data; Determine whether the historical setting sequence of each parameter group in the target historical usage data contains the target setting sequence; If the historical setting sequence includes the target setting sequence, the corresponding parameter group is used as the recommended parameter group.

[0061] It should be noted that there are many parameters to be set, for example, 10, but only 4 parameters may be set, and there are 6 unset parameters. Therefore, the target setting sequence has only 4 parameter setting sequences, while the historical setting sequence has 10 parameter setting sequences, so the length of the historical setting sequence is greater than the target setting sequence.

[0062] Based on this, when filtering the recommended parameter groups, it is necessary to determine whether the historical setting sequence contains the target setting sequence, rather than whether it is equivalent to the target setting sequence. It should be noted that inclusion means that there is a sequence in the historical setting sequence that is exactly the same and continuous as the target setting sequence. For example, if the target setting sequence is 1234, if the historical setting sequence is 15234, it is not considered that the historical setting sequence contains the target setting sequence, because 1234 in the historical setting sequence does not appear continuously.

[0063] With this implementation, each parameter group includes a historical setting sequence. The target historical usage data is first determined according to the target area, and then the target setting sequence is searched in the historical setting sequence corresponding to the target historical usage data to obtain a recommended parameter group. This is convenient and quick, and helps to ensure the accuracy of the recommended parameter group according to the target area.

[0064] Optionally, in an implementation of this embodiment, the set parameter, the value corresponding to the set parameter, the historical setting parameter and the historical value are represented by characters, the target setting sequence is formed by arranging the characters corresponding to the set parameter to form a first character string, and the historical setting sequence is formed by arranging the characters corresponding to the historical setting parameter to form a second character string; Determine whether the historical setting sequence of each parameter group in the target historical usage data contains the target setting sequence, including: It is determined whether the second character string corresponding to each parameter group in the target historical usage data contains the first character string.

[0065] Among them, the characters can be letters, numbers, symbols or Chinese characters, and the embodiments do not specifically limit this. It should be noted that the character structures and character types corresponding to different parameters and values ​​should be fixed, so that the characters corresponding to the same parameters are the same, and the characters corresponding to the same values ​​of the same parameters should also be the same. For example, the character corresponding to the operating mode is 1, then whether in the first character string or the second character string, the 1 that appears represents the operating mode and has a unique meaning.

[0066] It should be noted that for the same parameter, the same value should correspond to the same characters, but in the case of different parameters, the characters used can be repeated. For example, for the operating mode, a represents the mixed mode and b represents the quick wash mode, and characters with different values ​​cannot be repeated. However, the characters corresponding to the value of the temperature parameter can still use a and b. For example, a represents 30 degrees Celsius and b represents 40 degrees Celsius. Although the characters are the same, because the corresponding parameters are different, the meaning of the characters can be distinguished, and there is no restriction on the characters corresponding to the values ​​under different parameters.

[0067] With this implementation, the set parameters, values, historical set parameters and historical values ​​are all represented by characters. On the one hand, characters take up less space, which helps to reduce memory usage. On the other hand, the process of determining whether the target device set sequence is included in the historical set sequence is changed to determining whether the second string contains the first string. This makes comparison between strings more convenient and helps to improve efficiency.

[0068] Optionally, in an implementation of this embodiment, determining whether the second character string corresponding to each parameter group in the target historical usage data contains the first character string includes: Search the first character of the first string in the second string. If the first character of the first string exists in the second string, number the first characters in the order in which they appear in the second string to obtain the positions of n first characters, where n is a positive integer. Compare the characters after the first character in the second string one by one to see if they are the same as the corresponding characters in the first string; If the characters after the first character in the second character string are all the same as the corresponding characters in the first character string, it is determined that the second character string contains the first character string; If any character after the first first character in the second string is different from the corresponding character in the first string, then compare the characters after the second first character one by one to see if they are the same as the corresponding characters in the first string, until the characters after the last first character are compared with the corresponding characters in the first string; If any character after the last first character in the second character string is different from the corresponding character in the first character string, it is determined that the second character string does not contain the first character string.

[0069] For ease of understanding, for example, if the first string is: 1234, and its first character is 1, then search for 1 in the second string, and number them in the order in which 1 appears to obtain multiple positions of 1. For example, if the second string is: 135481058412348513, then the positions of 4 first characters can be obtained, and these 4 positions are numbered in the order in which they appear to obtain the first position to the fourth position. Then start comparing from the first position. The comparison process is: compare the second character in the first string with the first character after the first position in the second string, compare the third character in the first string with the second character after the first position in the second string, and so on. If all are the same, the comparison ends and it is determined that the second string contains the first string.

[0070] As long as there is one character that is different, switch to the second position for comparison until the fourth position is compared. For example, the first character after the first position is 3, which is different from 2 in the first string, then switch to the second position, the first character after the second position is 0, which is different from 2 in the first string, then switch to the third position, the first character after the third position is 2, which is the same as 2 in the first string, and then compare whether the second character after the third position is 3. If the result is yes, then compare whether the third character after the third position is 4. If the result is yes, it is determined that the second string contains the first string.

[0071] By adopting this implementation method, the position where the first character appears in the second character string is first determined, and then a search is performed according to the position, which is beneficial to reducing the number of searches and improving search efficiency, thereby helping to improve parameter recommendation efficiency.

[0072] Optionally, in an implementation of this embodiment, determining whether the second character string corresponding to each parameter group in the target historical usage data contains the first character string includes: The first character string is compared with the second character string corresponding to each parameter group in the target historical usage data using a preset character string matching algorithm to determine whether the second character string contains the first character string.

[0073] Among them, the string matching algorithm includes the KMP (Knuth-Morris-Pratt) algorithm.

[0074] By adopting this implementation method, the string matching algorithm can reduce the number of searches, which is beneficial to reducing the number of searches and improving search efficiency, thereby helping to improve parameter recommendation efficiency.

[0075] Optionally, in an implementation of this embodiment, the method further includes: Whenever the position of the nth first character is changed to the position of the next first character, the number of changes is accumulated to obtain the number of changes corresponding to each second character string; The corresponding recommended parameter groups are arranged according to the number of changes, so as to recommend the recommended parameters and the recommended values ​​according to the arrangement order of the recommended parameter groups.

[0076] That is, each second character string corresponds to a number of changes, and the recommended parameter group with the least or most number of changes is arranged at the top for recommendation.

[0077] With this implementation, the fewer the number of changes, the higher the similarity of the corresponding recommended parameter groups, which is conducive to arranging the recommended parameters and recommended values ​​of the recommended parameter group with the highest similarity in the front, thereby improving the user experience.

[0078] Optionally, in an implementation of this embodiment, taking a parameter in the recommended parameter group that is different from the set parameter as a recommended parameter and taking a value corresponding to the recommended parameter as a recommended value for recommendation includes: After converting the recommended parameters and the recommended values ​​from corresponding characters to corresponding identifiers according to a preset character replacement table, the recommendation is made, wherein the identifier includes at least one of text and numbers, wherein the type of text is determined according to the target area.

[0079] Before making a recommendation, the characters are replaced with text, numbers or symbols through a character replacement table to facilitate user viewing and understanding.

[0080] It should be noted that, since different regions may use different types of characters and numbers, when replacing, the characters and numbers are replaced according to the types used in the target region.

[0081] By adopting this implementation method, after characters are replaced with words and numbers, it is easy for users to understand and helps to improve the user experience.

[0082] Optionally, in an implementation of this embodiment, the method further includes: Determining a standby operation mode of the laundry treatment device according to the set parameters and the target area; Determine the parameters to be set according to the operation mode to be run; Compare the parameters to be set with the parameters to be set to obtain the parameters to be recommended; If the parameters to be recommended include parameters to be recommended that have multiple characters, the recommended parameter group is screened from the historical usage data of the corresponding region according to the target setting sequence and the target region; If all the parameters to be recommended are single-character parameters to be recommended, a parameter group including the mode to be operated is determined from the historical usage data corresponding to the target area, and the parameter group with the most usage times is recommended.

[0083] That is to say, in some application scenarios, the mode to be run will be determined first. Since different modes to be run require different parameters to be set, after the mode to be run is determined, the parameters to be set that need to be set can be determined. Then the parameters to be set are compared with the set parameters. If the parameters to be set are the same as the set parameters and the number of parameters is not greater than the number of parameters of the set parameters, no recommendation is made. Otherwise, a recommendation is made.

[0084] When making recommendations, it is first determined whether the parameter to be recommended contains multiple characters. Only when it contains multiple characters, the recommended parameter group is filtered according to the target setting order.

[0085] The difference between multi-bit characters and single characters is the number of characters. For example, 0000 is a 4-bit character, and 0 is a single character. As for which parameter in the recommended parameters is a multi-bit character, it can be found from a preset character replacement table.

[0086] With this implementation, for a single character, recommendations are made directly based on the number of times it is used, which helps to improve recommendation efficiency.

[0087] Optionally, in an implementation of this embodiment, the method further includes: If the clothing processing device is in a parameter waiting state, determining whether there is a setting operation; If there is no setting operation, the parameter group that is used most frequently in the historical usage data is recommended according to the target area.

[0088] With this implementation, in the case where no setting operation is performed, the parameter group that is used most frequently in the historical usage data corresponding to the target area is directly recommended to the user. For users who always use the same set of parameters, this is beneficial to improving the accuracy and efficiency of parameter recommendations.

[0089] Optionally, in an implementation of this embodiment, before filtering out a recommended parameter group from historical usage data of a corresponding region according to the target setting sequence and the target region, the method further includes: At least one identification parameter corresponding to the set parameter is obtained, including a key pressing area, a key pressing strength, a time interval between two adjacent keys being triggered, and a total setting time length of the set parameter.

[0090] The clothing processing device includes a button for the user to input the value of the parameter, and the button can be a physical mechanical button or a virtual button in the touch screen, which is not specifically limited in this embodiment. When the user inputs the value of each parameter through the button, the parameters such as the button pressing area, the button pressing force, the time interval between two adjacent buttons being triggered, and the total setting time of the set parameter are synchronously collected to obtain the identification parameter.

[0091] The button pressing area and the button pressing force can be collected by setting corresponding sensors. Preferably, the button pressing area is detected by a pressure sensor, and the button pressing force is detected by a pressure sensor.

[0092] It should be noted that the key pressing area, key pressing strength, time interval between two adjacent keys being triggered and total setting duration of the set parameters corresponding to different users may be different, so the user identity can be identified using the identification parameters.

[0093] A user identity is determined based on the identification parameters and the target setting sequence.

[0094] After obtaining the identification parameters, combined with the target setting sequence, the user identity is determined from two aspects, so that the accuracy of the user identity is improved. Specifically, in one embodiment, the identification parameters and the target setting sequence can be continuously collected during the user's use of the clothing processing device. After the amount of data reaches a certain level, multiple users can be distinguished, thereby generating corresponding user identities. In another embodiment, the user identity is input by the user himself or the user identity is identified by the user himself with the help of fingerprint recognition, face recognition and other technologies. After obtaining the user identity, the user's identification parameters and target setting sequence are collected and used as reference data. When it is necessary to identify the user identity, the collected identification parameters and target setting sequence can be compared with the reference data.

[0095] Different parameter recommendation strategies are used to recommend parameters according to the historical usage of the clothing processing device, the current day usage of the clothing processing device and the identity of the user.

[0096] Different clothing treatment devices have different historical usage, and the usage on the day may also be different. Among them, the historical usage is used to determine the number of times the clothing treatment device is used every day, and the usage on the day is used to determine the number of times the clothing treatment device is used on the day and the identity of the user at each use. Based on this, a variety of different situations can be divided, for example, divided into three situations, one is that the clothing treatment device was only used once every day in the past, which proves that all the clothes in the family are washed together; the second is that the clothing treatment device was used twice or more every day in the past and the user who used the clothing treatment device on the day was the same person, which proves that the person responsible for washing clothes in the family is fixed, but the clothes may be different, such as washing clothes that are not easy to dry in the morning and washing clothes that are easy to dry in the evening; the third is that the clothing treatment device was used twice or more every day in the past and the users who used the clothing treatment device on the day were different people, which proves that everyone in the family is responsible for washing their own clothes.

[0097] Based on the above classification, different parameter recommendation strategies can be used to recommend parameters, so that the recommended parameters are more in line with the actual needs and usage habits of users.

[0098] This implementation method uses identification parameters and target setting sequence to determine the user identity, which helps to improve the accuracy of the user identity. After determining the user identity, different parameter recommendation strategies are used to recommend parameters based on historical usage, current usage, and user identity, so that more factors are considered in the parameter recommendation process, which is conducive to improving the accuracy of parameter recommendation.

[0099] Optionally, in an optional implementation of the embodiment of the present application, the parameter recommendation is performed using different parameter recommendation strategies according to the historical usage of the clothing processing device, the current usage of the clothing processing device and the user identity, including: If the historical usage indicates that the laundry treatment device is used once within a preset time period, the parameter recommendation strategy adopted is: executing the method of filtering out a recommended parameter group from the historical usage data of the corresponding region according to the target setting sequence and the target region; And / or, if the historical usage indicates that the clothing treatment device is used multiple times within a preset time period, and the usage on the day and the user identity indicate that the same user uses the clothing treatment device multiple times, the parameter recommendation strategy adopted is: recommending a parameter group corresponding to the user identity and the current time from the historical usage data of the clothing treatment device as a recommended parameter group; And / or, if the historical usage indicates that the clothing processing device is used multiple times within a preset time period, and the usage on the day and the user identity indicate that multiple users use the clothing processing device multiple times, the parameter recommendation strategy adopted is: based on the target setting order and at least one of the user's age group, gender and clothing type, a recommended parameter group is filtered out from the historical usage data of the clothing processing device for recommendation.

[0100] The time period is one day, or may be other time lengths, which is not specifically limited in this embodiment.

[0101] In the case where the clothing processing device is used only once, the steps in the aforementioned method, namely step S104 and subsequent steps, are executed, and this embodiment will not repeat them any more.

[0102] If the clothing processing device is used multiple times and the same user uses the clothing processing device multiple times on the same day, the parameter group corresponding to the user is searched in the historical usage data according to the current time (that is, the current time). It should be noted that the user's identity can be identified, proving that the user has a certain laundry routine, so the historical usage data also stores data on the user washing clothes multiple times in a day. For ease of understanding, for example, the current time is 7 o'clock, and the user identity is user A, then the parameter group corresponding to user A is first filtered out from the historical usage data, and then the parameter group used by the user before and after 7 o'clock is filtered out as the recommended parameter group. Among them, a fluctuation range can be preset before and after 7 o'clock, for example, 1 hour before and after, that is, 6-8 o'clock.

[0103] It should also be noted that after obtaining the recommended parameter group, the above step S106 is performed.

[0104] In the case where the clothing treatment device is used multiple times and is used multiple times by different users on the same day, that is, different users have used the clothing treatment device and the number of times used is at least 1, the user age group is obtained using face recognition technology, the gender is obtained using face recognition technology, and / or the clothing type is obtained using clothing type recognition technology, and then the parameter group corresponding to the user identity is screened out from the historical usage data, and then a secondary screening is performed based on the user age group, gender and / or clothing type corresponding to the parameter group to obtain the secondary screened parameter group, and finally a third screening is performed based on the target setting order. Preferably, the three screening processes are the same as the above step S104 and will not be repeated.

[0105] By adopting this implementation method, three parameter recommendation strategies can be set, so that during the parameter recommendation process, three different situations can be distinguished according to historical usage, number of times used on the day and user identity, so that the corresponding parameter recommendation strategy can be adopted for each situation, thereby improving the accuracy and flexibility of parameter recommendation.

[0106] Optionally, in an optional implementation of the embodiment of the present application, the method further includes: Determine whether the percentage of single usage of the laundry processing device in all the time periods in the historical usage exceeds a preset percentage threshold; If yes, determining that the historical usage indicates that the laundry treatment device is used once within a preset time period; Otherwise, it is determined that the historical usage indicates that the laundry treatment device has been used multiple times within a preset time period.

[0107] The percentage threshold is set according to actual needs, and the percentage threshold can be 100% or 90%, which is not specifically limited in this embodiment.

[0108] By adopting this implementation method, a percentage calculation method is used to determine whether the number of times the clothing processing device is used as represented by the historical usage is a single time or multiple times. The calculation is simple and helps to reduce the occupation of computing resources.

[0109] For ease of understanding, Figure 2 As shown, three of the conditions of "the area of ​​the button pressed, the pressure, the interval between pressing the same button and different buttons, the total time of multiple parameter inputs, and the KMP match of the button pressing sequence" during the user's use process are used to identify the user's identity. After identifying the user, based on the historical data of the washing machine, determine whether the washing machine is washed more than once a day in 90% of the total number of days the washing machine is used; if there is only one wash a day or multiple customers wash clothes multiple times, the corresponding washing parameters and washing programs are recommended to the user based on the KMP algorithm. If a single customer selects different washing parameters multiple times in a day, when the number of washes selected in a fixed time period is greater than 10%, the parameter selection recommendation is made based on the parameters selected in the time period; if the same customer selects the same and similar programs every time, the parameters and programs are recommended based on clothing recognition.

[0110] An embodiment of the present application also provides an electronic device, the electronic device comprising a memory and a processor; Memory for storing computer programs; The processor is used to execute the computer program to implement the steps of the above method.

[0111] According to a third aspect of an embodiment of the present application, there is provided a clothing processing device, comprising the above-mentioned electronic device or using the above-mentioned method to perform parameter recommendation.

[0112] An embodiment of the present application also provides a clothing processing device, including the above-mentioned electronic device or using the above-mentioned method to recommend parameters.

[0113] Optionally, in an implementation of this embodiment, the clothing processing device includes at least one of a washing machine, a drying machine, a washer-dryer and a clothing care machine.

[0114] For ease of understanding, Figure 3-6 As shown, the parameter recommendation method provided in the present application can quickly recommend parameter-related solutions to users based on the washing machine usage habits of users in different regions. The more parameters the user selects, the closer the recommended washing machine-related parameters are to the usage habits of users in the region based on the usage record data of the washing machine region, thereby improving the user's operating experience of using the washing machine.

[0115] Specifically, the washing machine parameter recommendation for a single area is based on the user data difference selection and processing based on the historical data of the usage pattern and frequency accumulation in this area, and the complex parameters are matched and recommended based on the KMP algorithm.

[0116] (Note: In computer programming languages, numbers (0~9), letters (a~z / A~Z), and punctuation marks (!@, etc.) all represent a specific character, and uppercase and lowercase English letters represent different characters. For example, a and A represent different characters in a computer.) like Figure 3 As shown, the present application hierarchicalizes and characterizes the modes and washing parameters of the washing machine to facilitate data processing and parameter recommendations based on user habits. The modes of the washing machine are always set as the first level and are represented by characters such as a~z, A~Z. Each mode is pre-defined to correspond to a specific character. Since the mode is the first level, its letter representation is arranged in the first place. Other parameters are characterized as the second level. However, due to the large amount of data, some parameters may not be fully represented by a single character, so multiple characters are introduced to represent them, such as Figure 3 The reservation time, spin speed, drying time, etc., or other complex washing machine parameters are characterized using multiple characters such as aaaa / aaab.

[0117] like Figure 4 As shown in the figure, after the user turns on the machine, parameter recommendations are made based on whether the user has selected a mode and parameter. (1) If the user has not selected any parameter, the recommendation is made based on the mode with the most usage records in the area and the parameter with the most usage records corresponding to the mode. (2) If the user selects a mode or a parameter, the first-level corresponding characters of the mode are pre-generated, and the second-level parameters are generated according to the order of user selection. The corresponding sequence parameter character sorting and its corresponding sorting code are generated, and the characters that are not selected or partially selected are recommended based on the data in the area; for example, if the user selects the following in sequence: Figure 3For example, if the user inputs 15 for the drying time but does not select a specific parameter, the KMP algorithm recommends character codes 0015 / 0115 / 0150 / 0155 based on the user input 15 for the drying time, and 0015 / 0115 / 0150 / 0155 for the drying time, then the parameter character sorting is 1436, and the alternative recommendation code is 8257. The corresponding sorting code is "mff0130+alternative recommended character". If the alternative recommended parameter is multiple characters, the KMP algorithm is used to perform multi-character matching recommendation. For example, if the user inputs 15 for the drying time but does not select a specific parameter, the KMP algorithm recommends character codes 0015 / 0115 / 0150 / 0155 based on the user input 15, and then the sorting recommendation is performed based on the historical number of times the area is used. The process of recommending parameters ends when the customer selects all parameters and starts the washing machine.

[0118] like Figure 5 As shown in the figure, the process of users selecting input parameters is a process of parameter sorting and selection, and also a process of making corresponding recommendations based on the differences in habits of each user. The mode is the first level, and the second-level parameters sort the user parameters in order according to the order in which the users select the parameters. The remaining input and unselected parameters are matched and limited based on the previously selected mode and parameters. After the limitation, the second half of the undetermined parameters are sorted and recommended. For complex parameters represented by multiple characters, they are matched and recommended through the KMP algorithm. Other single parameters are recommended based on the current mode and selected parameters.

[0119] like Figure 6 As shown in FIG. 1 , it is a KMP matching process of multiple character parameters. If part of the user input parameters represents characters abcac, character matching recommendation of corresponding parameters is performed according to the representative characters input by the user, and the characters with the highest familiarity are screened out for parameter recommendation. The highest familiarity is measured by the number of comparisons. For example, the first group of parameters are compared 38 times, the second group of parameters are compared 36 times, and the third group of parameters are compared 45 times. The second group of parameters has the highest familiarity and is used as the recommended parameters. In the matching process, the input representative characters are used to generate the next array of the KMP algorithm in advance. In the comparison process, character movement matching is performed according to the next array of the KMP algorithm, and the corresponding number of comparisons is counted to obtain the parameter recommendation with the highest familiarity.

[0120] In the above-mentioned embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The steps shown in the relevant flow chart can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flow chart, in some cases, the steps shown or described can be executed in an order different from that here. In other words, the order of steps described in the foregoing embodiments is only an example, and it is also within the scope of protection of the embodiments of the present application to reasonably adjust the order of steps based on the content of the embodiments of the present application.

[0121] The sequence of serial numbers or introduction of the embodiments of the present application is for description only and does not represent the superiority or inferiority of the embodiments.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0123] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0124] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0125] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital versatile disc (DVD)), or a semiconductor medium (e.g., a solid state disk (SSD)). It is worth noting that the computer-readable storage medium mentioned in the embodiment of the present application may be a non-volatile storage medium, in other words, a non-transient storage medium. It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.) and signals involved in the embodiments of this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions. For example, the scene data of the current frame in the three-dimensional virtual scene, the device information of the client, and the scene interaction information involved in the embodiments of this application are all obtained with full authorization.

[0126] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A parameter recommendation method for a clothes processing device, characterized in that: The method comprises: Acquiring a set parameter and a target region where the clothes treating device is located, wherein the set parameter includes a parameter having a value set based on a setting operation for a value of a parameter of the clothes treating device; Obtaining a target setting sequence according to the setting sequence of the set parameters; Filtering a recommended parameter group from historical usage data of a corresponding region according to the target setting sequence and the target region, wherein the historical usage data includes usage data of laundry processing equipment in the corresponding region; A parameter in the recommended parameter group that is different from the set parameter is recommended as a recommended parameter, and a value corresponding to the recommended parameter is recommended as a recommended value.

2. The parameter recommendation method for a clothes processing device according to claim 1, characterized in that: The historical usage data includes a plurality of parameter groups, each of which includes historical setting parameters having a historical setting sequence and historical values ​​corresponding to each of the historical setting parameters; The step of selecting a recommended parameter group from historical usage data of a corresponding region according to the target setting sequence and the target region includes: using the historical usage data of the clothes treating equipment in the target area as target historical usage data; Determining whether the historical setting sequence of each of the parameter groups in the target historical usage data includes the target setting sequence; If the historical setting sequence includes the target setting sequence, the corresponding parameter group is used as the recommended parameter group.

3. The parameter recommendation method for a clothes processing device according to claim 2, characterized in that: The set parameter, the value corresponding to the set parameter, the historical set parameter and the historical value are represented by characters, the target setting sequence is formed by arranging the characters corresponding to the set parameters to form a first character string, and the historical setting sequence is formed by arranging the characters corresponding to the historical set parameters to form a second character string; The determining whether the historical setting sequence of each parameter group in the target historical usage data includes the target setting sequence includes: It is determined whether the second character string corresponding to each of the parameter groups in the target historical usage data includes the first character string.

4. The parameter recommendation method for a clothes processing device according to claim 3, characterized in that: The determining whether the second character string corresponding to each of the parameter groups in the target historical usage data contains the first character string includes: Searching for the first character of the first string in the second string, if the first character of the first string exists in the second string, numbering the first characters in the order in which they appear in the second string to obtain n positions of the first characters, where n is a positive integer; Compare the characters after the first character in the second character string one by one to see if they are the same as the corresponding characters in the first character string; If the characters after the first character in the second character string are all the same as the corresponding characters in the first character string, it is determined that the second character string contains the first character string; If any character after the first position of the first character in the second character string is different from the corresponding character in the first character string, then compare the characters after the second position of the first character to see if they are the same as the corresponding characters in the first character string one by one, until the characters after the last position of the first character are compared with the corresponding characters in the first character string; If any character after the last first character in the second character string is different from the corresponding character in the first character string, it is determined that the second character string does not contain the first character string.

5. The parameter recommendation method for a clothes processing device according to claim 3, characterized in that: The determining whether the second character string corresponding to each of the parameter groups in the target historical usage data contains the first character string includes: The first character string is compared with the second character string corresponding to each of the parameter groups in the target historical usage data using a preset character string matching algorithm to determine whether the second character string contains the first character string.

6. The parameter recommendation method for a clothes processing device according to claim 4, characterized in that: The method further comprises: Whenever the position of the nth first character is changed to the position of the next first character, the number of changes is accumulated to obtain the number of changes corresponding to each second character string; The corresponding recommended parameter groups are arranged according to the number of changes, so as to recommend the recommended parameters and the recommended values ​​according to the arrangement order of the recommended parameter groups.

7. The parameter recommendation method for a clothes processing device according to claim 3, characterized in that: The taking a parameter in the recommended parameter group that is different from the set parameter as a recommended parameter and recommending a value corresponding to the recommended parameter as a recommended value includes: The recommendation is performed after converting the recommendation parameter and the recommendation value from the corresponding characters into corresponding identifiers according to a preset character replacement table, wherein the identifier includes at least one of text and numbers, and wherein the type of text is determined according to the target area.

8. The parameter recommendation method for a clothes processing device according to claim 3, characterized in that: The method further comprises: determining a standby operation mode of the laundry treatment device according to the set parameters and the target area; Determining the parameters to be set according to the waiting operation mode; Comparing the parameters to be set with the parameters to be set to obtain the parameters to be recommended; If the parameters to be recommended include the parameters to be recommended that are multi-character, then performing the step of filtering out a recommended parameter group from historical usage data of a corresponding region according to the target setting sequence and the target region; If the parameters to be recommended are all single-character parameters to be recommended, the parameter group including the mode to be operated is determined from the historical usage data corresponding to the target area, and the parameter group with the most usage times is recommended.

9. The parameter recommendation method for a clothes processing device according to claim 1, characterized in that: The method further comprises: If the laundry processing device is in a parameter waiting state, determining whether the setting operation is performed; If the setting operation is not performed, the parameter group that is used most frequently in the historical usage data is recommended according to the target area.

10. The parameter recommendation method for a clothes processing device according to claim 1, characterized in that: Before selecting a recommended parameter group from historical usage data of a corresponding region according to the target setting sequence and the target region, the method further includes: Acquire at least one identification parameter corresponding to the set parameter, including a key pressing area, a key pressing strength, a time interval between two adjacent keys being triggered, and a total setting duration of the set parameter; determining a user identity based on the identification parameters and the target setting sequence; Different parameter recommendation strategies are used to recommend parameters according to the historical usage of the clothing processing device, the current day usage of the clothing processing device and the identity of the user.

11. The parameter recommendation method for a clothes processing device according to claim 10, characterized in that: The adopting different parameter recommendation strategies to perform parameter recommendation according to the historical usage of the clothing processing device, the current usage of the clothing processing device and the user identity includes: If the historical usage indicates that the laundry treatment device is used once within a preset time period, the parameter recommendation strategy adopted is: executing the method of filtering out a recommended parameter group from the historical usage data of the corresponding region according to the target setting sequence and the target region; And / or, if the historical usage indicates that the clothing treatment device is used multiple times within a preset time period, and the usage on the day and the user identity indicate that the same user uses the clothing treatment device multiple times, the parameter recommendation strategy adopted is: recommending a parameter group corresponding to the user identity and the current time from the historical usage data of the clothing treatment device as a recommended parameter group; And / or, if the historical usage indicates that the clothing processing device is used multiple times within a preset time period, and the usage on the day and the user identity indicate that multiple users use the clothing processing device multiple times, the parameter recommendation strategy adopted is: based on the target setting order and at least one of the user's age group, gender and clothing type, a recommended parameter group is filtered out from the historical usage data of the clothing processing device for recommendation.

12. The parameter recommendation method for a clothes processing device according to claim 11, characterized in that: The method further comprises: Determine whether the percentage of single usage of the laundry processing device in all the time periods in the historical usage exceeds a preset percentage threshold; If yes, determining that the historical usage indicates that the laundry treatment device is used once within a preset time period; Otherwise, it is determined that the historical usage indicates that the laundry treatment device has been used multiple times within a preset time period.

13. An electronic device, characterized in that: The electronic device comprises a memory and a processor; The memory is used to store computer programs; The processor is used to execute the computer program to implement the steps of the method according to any one of claims 1 to 12.

14. A clothes processing device, characterized in that: The electronic device comprising claim 13 or the method according to any one of claims 1 to 12 is used for parameter recommendation.

15. The clothes treating device according to claim 14, characterized in that: The clothing processing device includes at least one of a washing machine, a drying machine, a washing and drying machine and a clothing care machine.

Images